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• W2971354641 abstract "Abstract Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available." @default.
• W2971354641 created "2019-09-05" @default.
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• W2971354641 date "2019-08-31" @default.
• W2971354641 modified "2023-10-06" @default.
• W2971354641 title "qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data" @default.
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• W2971354641 doi "https://doi.org/10.1101/751370" @default.
• W2971354641 hasPublicationYear "2019" @default.
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